# cond.quantile: Conditional quantile In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

 cond.quantile R Documentation

## Conditional quantile

### Description

Computes the quantile for conditional distribution function.

### Usage

```cond.quantile(
qua = 0.5,
fdata0,
fdataobj,
y,
fn,
a = min(y),
b = max(y),
tol = 10^floor(log10(max(y) - min(y)) - 3),
iter.max = 100,
...
)
```

### Arguments

 `qua` Quantile value, by default the median (`qua`=0.5). `fdata0` Conditional functional explanatory data of `fdata` class object. `fdataobj` Functional explanatory data of `fdata` class object. `y` Scalar Response. `fn` Conditional distribution function. `a` Lower limit. `b` Upper limit. `tol` Tolerance. `iter.max` Maximum iterations allowed, by default `100`. `...` Further arguments passed to or from other methods.

### Value

Return the quantile for conditional distribution function.

### Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@udc.es

### References

Ferraty, F. and Vieu, P. (2006). Nonparametric functional data analysis. Springer Series in Statistics, New York.

See Also as: `cond.F` and `cond.mode`.

### Examples

```## Not run:
n= 100
t= seq(0,1,len=101)
beta = t*sin(2*pi*t)^2
x = matrix(NA, ncol=101, nrow=n)
y=numeric(n)
x0<-rproc2fdata(n,seq(0,1,len=101),sigma="wiener")
x1<-rproc2fdata(n,seq(0,1,len=101),sigma=0.1)
x<-x0*3+x1
fbeta = fdata(beta,t)
y<-inprod.fdata(x,fbeta)+rnorm(n,sd=0.1)

prx=x[1:50];pry=y[1:50]
ind=50+1;ind2=51:60
pr0=x[ind];pr10=x[ind2]
ndist=161
gridy=seq(-1.598069,1.598069, len=ndist)
ind4=5
y0 = gridy[ind4]

# Conditional median
med=cond.quantile(qua=0.5,fdata0=pr0,fdataobj=prx,y=pry,fn=cond.F,h=1)

# Conditional CI 95% conditional
lo=cond.quantile(qua=0.025,fdata0=pr0,fdataobj=prx,y=pry,fn=cond.F,h=1)
up=cond.quantile(qua=0.975,fdata0=pr0,fdataobj=prx,y=pry,fn=cond.F,h=1)
print(c(lo,med,up))

## End(Not run)

```

fda.usc documentation built on Oct. 17, 2022, 9:06 a.m.